@article{ADMIN,
      recid = {45},
      title = {SUSTAINABLESIGNALS: An AI Approach for Inferring Consumer  Product Sustainability},
      address = {2023-05-25},
      number = {ADMIN},
      abstract = {The everyday consumption of household goods is a  significant source of environmental pollution. As people  increasingly shop online, this affords an opportunity to  provide consumers with actionable feedback on the social  and environmental impact of potential purchases. In our  work, we explore the following questions on Amazon a) do  consumers bring up the environment in their reviews either  directly or through relevant related topics? b) do they  tend to bring up the environment when they are satisfied or  dissatisfied with a product? c) in what granular context do  they bring up the environment?
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To address these  questions, we designed an annotation task and recruited  knowledgeable students to annotate consumer product  reviews. This dataset comprises annotations for 779  individual reviews, with each review corresponding to a  distinct product.
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In our paper, we propose a  machine learning method using these annotations that can  discover signals of sustainability and infer a product's  sustainability score. Our model and code are released at <a  href="https://github.com/Sabina321/sustainable_signals">https://github.com/Sabina321/sustainable_signals</a>.  The data is for the following paper: Tong Lin, Tianliang  Xu, Amit Zac, and Sabina Tomkins. SUSTAINABLESIGNALS: An AI  Approach for Inferring Consumer Product Sustainability. In  Proceedings of the Thirty-Second International Joint  Conference on Artificial Intelligence, 2023. AI and Social  Good Track.},
      url = {http://socialmediaarchive.org/record/45},
}